Machine learning methods are used in the classification of various cardiovascular diseases through ECG data analysis. The concept of varying subcutaneous implantable cardiac defibrillator (S-ICD) eligibility, owing to the dynamicity of ECG signals, has been introduced before. There are practical limitations to acquiring longer durations of ECG signals for S-ICD screening. This study explored the potential use of deep learning methods in S-ICD screening.

中文翻译：

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深度学习方法在S-ICD筛查中的相关性分析

机器学习方法通​​过心电图数据分析用于各种心血管疾病的分类。由于心电图信号的动态性，之前已经介绍过不同皮下植入式心脏除颤器 (S-ICD) 资格的概念。获取较长持续时间的心电图信号以进行 S-ICD 筛查存在实际限制。本研究探讨了深度学习方法在 S-ICD 筛查中的潜在用途。